SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Showing 15511600 of 6771 papers

TitleStatusHype
Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties0
Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data0
LiD-FL: Towards List-Decodable Federated LearningCode0
Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy0
Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks0
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review0
Tackling Noisy Clients in Federated Learning with End-to-end Label CorrectionCode1
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data0
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy0
SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG0
Masked Random Noise for Communication Efficient Federated LearningCode0
FedBAT: Communication-Efficient Federated Learning via Learnable BinarizationCode0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense0
Strategic Federated Learning: Application to Smart Meter Data Clustering0
Model Hijacking Attack in Federated Learning0
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning0
Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning0
TreeCSS: An Efficient Framework for Vertical Federated Learning0
Load Balancing in Federated Learning0
Mobility-Aware Federated Self-supervised Learning in Vehicular Network0
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity0
FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication0
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy InsightsCode0
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning0
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing0
UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification TasksCode0
Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction0
F-KANs: Federated Kolmogorov-Arnold NetworksCode0
FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models0
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel ExtractionCode1
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain0
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering0
A collaborative ensemble construction method for federated random forest0
Reducing Spurious Correlation for Federated Domain Generalization0
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification0
FLUE: Federated Learning with Un-Encrypted model weights0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction0
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning0
FADAS: Towards Federated Adaptive Asynchronous OptimizationCode0
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future0
SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment0
Sparse Incremental Aggregation in Multi-Hop Federated Learning0
Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review0
Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets0
HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified